Issue 8.8

Issue 8.10 is now online!

The October issue of Methods is now online!

This double-sized issue contains three Applications articles and two Open Access articles. These five papers are freely available to everyone, no subscription required.

 Phylogenetic TreesThe fields of phylogenetic tree and network inference have advanced independently, with only a few attempts to bridge them. Schliep et al. provide a framework, implemented in R, to transfer information between trees and networks.

 Emon: Studies, surveys and monitoring are often costly, so small investments in preliminary data collection and systematic planning of these activities can help to make best use of resources. To meet recognised needs for accessible tools to plan some aspects of studies, surveys and monitoring, Barry et al. developed the R package emon, which includes routines for study design through power analysis and feature detection.

 Haplostrips: A tool to visualise polymorphisms of a given region of the genome in the form of independently clustered and sorted haplotypes. Haplostrips is a command-line tool written in Python and R, that uses variant call format files as input and generates a heatmap view.

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A New Way to Study Bee Cognition in the Wild

Understanding how animals perceive, learn and remember stimuli is critical for understanding both how cognition is shaped by natural selection, and how ecological factors impact behaviour.Unfortunately, the limited number of protocols currently available for studying insect cognition has restricted research to a few commercially available bee species, in almost exclusively laboratory settings.
In a new video Felicity Muth describes a simple method she developed with Trenton Cooper, Rene Bonilla and Anne Leonard for testing both lab- and wild-caught bees for their preferences, learning and memory. They hope this method will be useful for students and researchers who have not worked on cognition in bees before. The video includes a tutorial for carrying out the method and describes the data presented in their Methods in Ecology and Evolution article, also titled ‘A novel protocol for studying bee cognition in the wild‘.

This video is based on the article ‘A novel protocol for studying bee cognition in the wild by Muth et al.

 

Learn to be a Reviewer: Peer Reviewer Mentoring Scheme

Today is the first day of peer review week. One of the issues that many people bring up about the current system of peer review is that there is very little formal training. There are guidance documents available (including the BES Guide to Peer Review), workshops on peer review can be found at some conferences and some senior academics teach their PhD students or post-docs about the process. In general though, peer review training is fairly hard to come by.

This is something that people have told us (the BES publications team) at conferences and through surveys, so we’re doing something about it. From October 2017 until April 2018 Methods in Ecology and Evolution is going to be partnering with the BES Quantitative Ecology Special Interest Group to run a trial Peer Review Mentoring Scheme.

The trial scheme is going to focus on statistical ecology (as we receive a lot of statistical papers at Methods in Ecology and Evolution), but if it goes well, we’ll be looking at other areas of expertise too.

Applications for Mentor and Mentee positions are now open. If you’re an experienced statistical ecologist or evolutionary biologist or an Early Career Researcher in those fields, we’d love to receive an application from you. Continue reading

Issue 8.9

Issue 8.9 is now online!

The September issue of Methods is now online!

This issue contains two Applications articles and three Open Access articles. These five papers are freely available to everyone, no subscription required.

 qfasar: A new R package for diet estimation using quantitative fatty acid signature analysis methods. It also provides functionality to evaluate and potentially improve the performance of a library of prey signature data, compute goodness-of-fit diagnostics, and support simulation-based research.

 biomass: An r package designed to compute both AGB/AGC estimate and its associated uncertainty from forest plot datasets, using a Bayesian inference procedure. The package builds upon previous work on pantropical and regional biomass allometric equations and published datasets by default, but it can also integrate unpublished or complementary datasets in many steps.

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Issue 8.8

Issue 8.8 is now online!

The August issue of Methods is now online!

This issue contains two Applications articles and two Open Access articles. These four papers are freely available to everyone, no subscription required.

 Paco: An R package that assesses the phylogenetic congruence, or evolutionary dependence, of two groups of interacting species using both ecological interaction networks and their phylogenetic history.

 Open MEE: Open Meta-analyst for Ecology and Evolution (Open MEE) addresses the need for advanced, easy-to-use software for meta-analysis and meta-regression.It offers a suite of advanced meta-analysis and meta-regression methods for synthesizing continuous and categorical data, including meta-regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation.

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Conditional Occupancy Design Explained

Occupancy surveys are widely used in ecology to study wildlife and plant habitat use. To account for imperfect detection probability many researchers use occupancy models. But occupancy probability estimates for rare species tend to be biased because we’re unlikely to observe the animals at all and as a result, the data aren’t very informative.

In their new article – ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ – Specht et al. developed a new “conditional” occupancy survey design to improve occupancy estimates for rare species, They also compare it to standard and removal occupancy study designs. In this video two of the authors, Hannah Specht and Henry Reich, explain how their new conditional occupancy survey design works. 

This video is based on the article ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ by Specht et al.

 

Issue 8.7

Issue 8.7 is now online!

© Paula Matos

The July issue of Methods is now online!

This issue contains three Applications articles (one of which is Open Access) and one additional Open Access article. These four papers are freely available to everyone, no subscription required.

BioEnergeticFoodWebs: An implementation of Yodzis & Innes bio-energetic model, in the high-performance computing language Julia. This package can be used to conduct numerical experiments in a reproducible and standard way.

 Controlled plant crosses: Chambers which allow you to control pollen movement and paternity of offspring using unpollinated isolated plants and microsatellite markers for parents and their putative offspring. This system has per plant costs and efficacy superior to pollen bags used in past studies of wind-pollinated plants.

 The Global Pollen Project: The study of fossil and modern pollen assemblages provides essential information about vegetation dynamics in space and time. In this Open Access Applications article, Martin and Harvey present a new online tool – the Global Pollen Project – which aims to enable people to share and identify pollen grains. Through this, it will create an open, free and accessible reference library for pollen identification. The database currently holds information for over 1500 species, from Europe, the Americas and Asia. As the collection grows, we envision easier pollen identification, and greater use of the database for novel research on pollen morphology and other characteristics, especially when linked to other palaeoecological databases, such as Neotoma.

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Why Soft Sweeps from Standing Genetic Variation are More Likely than You May Think

We coined the term “soft sweeps” in 2005. The term has since become widely used, though not everyone uses the term in the same way. As part of the ‘How to Measure Natural Selection‘ Special Feature in Methods in Ecology and Evolution, we attempt to clarify what “soft sweep” means and doesn’t mean. For example, not every sweep from standing genetic variation is necessarily a soft sweep.
In the review paper we also show under what conditions soft sweeps are likely (e.g., high population-wide mutation rate, multi-locus selection target). Finally, we describe relevant examples in fruitflies, humans and microbes and we discuss future research directions.
The video focuses on one aspect of the paper, which is illustrated in figure 3: “Why soft sweeps from standing genetic variation are more likely than you may think.”

This video is based on the Open Access article ‘Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation by Hermisson and Pennings in the ‘How to Measure Natural Selection‘ Special Feature.

 

Issue 8.6: How to Measure Natural Selection

Issue 8.6 is now online!

The April issue of Methods, which includes our latest Special Feature – ‘How to Measure Natural Selection – is now online!

Understanding how and why some individuals survive and reproduce better than others, the traits that allow them to do so, the genetic basis of those traits, and the signatures of past and present selection in patterns of variation in the genome remain at the top of the research agenda for evolutionary biology. This Special Feature – Guest Edited by Jeff Conner, John Stinchcombe and Joanna Kelley – draws together a collection of seven papers that highlight new methodological and conceptual approaches to meeting this agenda.

Three of the ‘How to Measure Natural Selection’ papers – Franklin and Morrissey, Thomson and Hadfield, and Hadfield and Thomson – clarify unresolved aspects of the literature in meaningful and important ways. Following on from this Hermisson and Pennings; Lotterhos et al.; and Villanueva‐Cañas et al. tackle the genomic results of evolution by natural selection: namely, how we can detect natural selection from genomic data? Finally, Wadgymar et al. address the issue of how much we know about the underlying loci or agents of selection.

To use the Editors’ own words, the articles in this issue “deal with how we can detect selection in a way that can be used to predict evolutionary responses, how selection affects the genome, and how selection and genetics underlie adaptive differentiation.”

All of the articles in the ‘How to Measure Natural Selection‘ Special Feature will be freely available for a limited time.
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